Abstract
The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.
Highlights
Winter wheat is one of the most important crops in the north of China, and it is usually cultivated with the right amount of nitrogen to achieve a high output
The flowchart of modeling and analyzing for leaf chlorophyll content estimationofofwinter winter wheat and near-infrared spectroscopy is given in Figure content estimation wheat based basedononvisible visible and near-infrared spectroscopy is given in 1, which gives a new approach to the study of wheat spectrum
Comparing the predictive efficiency based on Partial least squares (PLS) and BP neural network (BPNN) model, and the preprocessing using multiplicative scatter correction (MSC), standard normal variable transformation (SNV), sliding window smoothing (SWS)-MSC, and SWS-SNV, it can be seen that the preprocessing using SWS-MSC
Summary
Winter wheat is one of the most important crops in the north of China, and it is usually cultivated with the right amount of nitrogen to achieve a high output. The value of leaf chlorophyll content can help to understand nutritional status of the plant, and scientifically guide the fertilization management to ensure a good crop quality and yield [3,4]. This practice has an important significance for the modern precision agriculture. Spectrophotometric method, a traditional destructive method used in the laboratory, is based on the technique that measures leaf chlorophyll concentration by organic extraction and spectrophotometric analysis This destructive approach is accurate and is considered as a benchmark for the estimation of chlorophyll content. It requires special equipment, which is Sensors 2016, 16, 437; doi:10.3390/s16040437 www.mdpi.com/journal/sensors
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